Assessing Representation Learning and Clustering Algorithms for Computer-Assisted Image Annotation—Simulating and Benchmarking MorphoCluster
Abstract
Image annotation is a time-consuming and costly task. Previously, we published MorphoCluster
as a novel image annotation tool to address problems of conventional, classifier-based
image annotation approaches: their limited efficiency, training set bias and lack of novelty detection.
MorphoCluster uses clustering and similarity search to enable efficient, computer-assisted image
annotation. In this work, we provide a deeper analysis of this approach. We simulate the actions of
a MorphoCluster user to avoid extensive manual annotation runs. This simulation is used to test
supervised, unsupervised and transfer representation learning approaches. Furthermore, shrunken
k-means and partially labeled k-means, two new clustering algorithms that are tailored specifically
for the MorphoCluster approach, are compared to the previously used HDBSCAN*. We find that
labeled training data improve the image representations, that unsupervised learning beats transfer
learning and that all three clustering algorithms are viable options, depending on whether completeness,
efficiency or runtime is the priority. The simulation results support our earlier finding
that MorphoCluster is very efficient and precise. Within the simulation, more than five objects per
simulated click are being annotated with 95% precision.
Domains
Environmental Sciences
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